Background

This file is designed to use CDC data to assess coronavirus disease burden by state, including creating and analyzing state-level clusters.

Through March 7, 2021, The COVID Tracking Project collected and integrated data on tests, cases, hospitalizations, deaths, and the like by state and date. The latest code for using this data is available in Coronavirus_Statistics_CTP_v004.Rmd.

The COVID Tracking Project suggest that US federal data sources are now sufficiently robust to be used for analyses that previously relied on COVID Tracking Project. This code is an attempt to update modules in Coronavirus_Statistics_CTP_v004.Rmd to leverage US federal data.

The code in this module builds on code available in _v004, with function and mapping files updated:

Broadly, the CDC data analyzed by this module includes:

Functions and Mapping Files

The tidyverse package is loaded and functions are sourced:

# The tidyverse functions are routinely used without package::function format
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6     ✔ purrr   0.3.4
## ✔ tibble  3.1.8     ✔ dplyr   1.0.9
## ✔ tidyr   1.2.0     ✔ stringr 1.4.0
## ✔ readr   2.1.2     ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
library(geofacet)

# Functions are available in source file
source("./Generic_Added_Utility_Functions_202105_v001.R")
source("./Coronavirus_CDC_Daily_Functions_v002.R")

A series of mapping files are also available to allow for parameterized processing. Mappings include:

These default parameters are maintained in a separate .R file and can be sourced:

source("./Coronavirus_CDC_Daily_Default_Mappings_v002.R")

Example Code Processing

The function is run to download and process the latest CDC case, hospitalization, and death data:

readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_220907.csv", 
                 "cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_220907.csv", 
                 "vax"="./RInputFiles/Coronavirus/vaxData_downloaded_220907.csv"
                 )
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220805")$dfRaw$cdcDaily, 
                    "cdcHosp"=readFromRDS("cdc_daily_220805")$dfRaw$cdcHosp, 
                    "vax"=readFromRDS("cdc_daily_220805")$dfRaw$vax
                    )

cdc_daily_220907 <- readRunCDCDaily(thruLabel="Sep 05, 2022", 
                                    downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x), 
                                    readFrom=readList,
                                    compareFile=compareList, 
                                    writeLog=NULL, 
                                    useClusters=readFromRDS("cdc_daily_210528")$useClusters, 
                                    weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7", 
                                                       "vxcpm7", "vxcgte65pct"
                                                       ),
                                    skipAssessmentPlots=FALSE, 
                                    brewPalette="Paired"
                                    )
## Rows: 57480 Columns: 15
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (5): submission_date, state, created_at, consent_cases, consent_deaths
## dbl (10): tot_cases, conf_cases, prob_cases, new_case, pnew_case, tot_death,...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 33
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 5 and at least 5%
## 
##          date       name newValue refValue absDelta   pctDelta
## 1  2022-07-31 new_deaths      116       23       93 1.33812950
## 2  2022-07-30 new_deaths      130       34       96 1.17073171
## 3  2022-07-23 new_deaths      158      109       49 0.36704120
## 4  2022-07-24 new_deaths      170      126       44 0.29729730
## 5  2022-08-01 new_deaths      433      347       86 0.22051282
## 6  2022-07-28 new_deaths      518      434       84 0.17647059
## 7  2022-07-16 new_deaths      151      127       24 0.17266187
## 8  2022-07-29 new_deaths      639      543       96 0.16243655
## 9  2022-07-25 new_deaths      306      265       41 0.14360771
## 10 2022-08-03 new_deaths      716      632       84 0.12462908
## 11 2022-08-02 new_deaths      715      632       83 0.12323682
## 12 2022-07-27 new_deaths      703      634       69 0.10321616
## 13 2022-07-10 new_deaths      114      103       11 0.10138249
## 14 2022-07-22 new_deaths      628      580       48 0.07947020
## 15 2022-06-18 new_deaths      105       97        8 0.07920792
## 16 2022-07-26 new_deaths      643      596       47 0.07586764
## 17 2022-07-04 new_deaths      138      128       10 0.07518797
## 18 2022-07-18 new_deaths      361      337       24 0.06876791
## 19 2022-07-21 new_deaths      500      471       29 0.05973223
## 20 2022-07-09 new_deaths      110      104        6 0.05607477
## 21 2022-07-30  new_cases    38338    32823     5515 0.15500063
## 22 2022-07-31  new_cases    39803    35276     4527 0.12059298
## 23 2022-08-01  new_cases   126242   133013     6771 0.05223429

## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
##    state       name newValue refValue absDelta    pctDelta
## 1     NC tot_deaths 11352019 11325521    26498 0.002336938
## 2     KY tot_deaths  6954858  6939424    15434 0.002221633
## 3     NC new_deaths    26101    25692      409 0.015793640
## 4     KY new_deaths    16647    16438      209 0.012634124
## 5     FL new_deaths    78609    77823      786 0.010049095
## 6     AL new_deaths    20081    19974      107 0.005342654
## 7     SC new_deaths    18211    18192       19 0.001043870
## 8     SC  new_cases  1626423  1605165    21258 0.013156380
## 9     KY  new_cases  1489715  1479668    10047 0.006767062
## 10    NC  new_cases  3026839  3022204     4635 0.001532474
## 
## 
## 
## Raw file for cdcDaily:
## Rows: 57,480
## Columns: 15
## $ date           <date> 2021-03-11, 2021-12-01, 2022-01-02, 2021-09-01, 2021-0…
## $ state          <chr> "KS", "ND", "AS", "ND", "IN", "FL", "TN", "PR", "PW", "…
## $ tot_cases      <dbl> 297229, 163565, 11, 118491, 668765, 3510205, 64885, 173…
## $ conf_cases     <dbl> 241035, 135705, NA, 107475, NA, NA, 64371, 144788, NA, …
## $ prob_cases     <dbl> 56194, 27860, NA, 11016, NA, NA, 514, 29179, NA, NA, NA…
## $ new_cases      <dbl> 0, 589, 0, 536, 487, 9979, 1816, 667, 0, 317, 0, 28, 8,…
## $ pnew_case      <dbl> 0, 220, 0, 66, 0, 2709, 30, 274, 0, 0, 0, 5, 0, 46, 70,…
## $ tot_deaths     <dbl> 4851, 1907, 0, 1562, 12710, 56036, 749, 2911, 0, 561, 0…
## $ conf_death     <dbl> NA, NA, NA, NA, 12315, NA, 722, 2482, NA, NA, NA, 1601,…
## $ prob_death     <dbl> NA, NA, NA, NA, 395, NA, 27, 429, NA, NA, NA, 366, NA, …
## $ new_deaths     <dbl> 0, 9, 0, 1, 7, 294, 8, 8, 0, 12, 0, 0, 0, 5, 0, 4, 0, 0…
## $ pnew_death     <dbl> 0, 0, 0, 0, 2, 26, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ created_at     <chr> "03/12/2021 03:20:13 PM", "12/02/2021 02:35:20 PM", "01…
## $ consent_cases  <chr> "Agree", "Agree", NA, "Agree", "Not agree", "Not agree"…
## $ consent_deaths <chr> "N/A", "Not agree", NA, "Not agree", "Agree", "Not agre…
## Rows: 49367 Columns: 135
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr    (1): state
## dbl  (132): critical_staffing_shortage_today_yes, critical_staffing_shortage...
## lgl    (1): geocoded_state
## date   (1): date
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 33
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 5 and at least 5%
## 
##         date     name newValue refValue absDelta  pctDelta
## 1 2020-07-25 hosp_ped     3964     4594      630 0.1472307

## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
##    state       name newValue refValue absDelta    pctDelta
## 1     ND        inp   122358   122070      288 0.002356522
## 2     NH   hosp_ped     1127     1167       40 0.034873583
## 3     KS   hosp_ped     4891     4725      166 0.034525790
## 4     ME   hosp_ped     2387     2338       49 0.020740741
## 5     KY   hosp_ped    20285    20665      380 0.018559219
## 6     WV   hosp_ped     5686     5753       67 0.011714311
## 7     VA   hosp_ped    18388    18192      196 0.010716238
## 8     TN   hosp_ped    22215    22423      208 0.009319414
## 9     NM   hosp_ped     8054     8114       60 0.007422068
## 10    SC   hosp_ped     9035     9092       57 0.006288961
## 11    DE   hosp_ped     5277     5310       33 0.006234061
## 12    NJ   hosp_ped    19499    19618      119 0.006084311
## 13    UT   hosp_ped    10271    10210       61 0.005956740
## 14    MS   hosp_ped    11803    11854       51 0.004311620
## 15    AL   hosp_ped    20947    21025       78 0.003716764
## 16    VT   hosp_ped      540      542        2 0.003696858
## 17    WY   hosp_ped      859      856        3 0.003498542
## 18    MA   hosp_ped    12619    12657       38 0.003006805
## 19    NC   hosp_ped    30541    30453       88 0.002885530
## 20    PR   hosp_ped    23021    22959       62 0.002696825
## 21    IL   hosp_ped    44084    44202      118 0.002673131
## 22    AK   hosp_ped     2664     2657        7 0.002631084
## 23    MO   hosp_ped    39841    39939       98 0.002456756
## 24    PA   hosp_ped    55078    55211      133 0.002411845
## 25    AR   hosp_ped    12767    12747       20 0.001567767
## 26    CO   hosp_ped    22421    22387       34 0.001517586
## 27    OH   hosp_ped    91382    91261      121 0.001324989
## 28    AZ   hosp_ped    27563    27532       31 0.001125329
## 29    MD   hosp_ped    17240    17221       19 0.001102696
## 30    ND hosp_adult   115920   115630      290 0.002504859
## 
## 
## 
## Raw file for cdcHosp:
## Rows: 49,367
## Columns: 135
## $ state                                                                        <chr> …
## $ date                                                                         <date> …
## $ critical_staffing_shortage_today_yes                                         <dbl> …
## $ critical_staffing_shortage_today_no                                          <dbl> …
## $ critical_staffing_shortage_today_not_reported                                <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_yes                       <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_no                        <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_not_reported              <dbl> …
## $ hospital_onset_covid                                                         <dbl> …
## $ hospital_onset_covid_coverage                                                <dbl> …
## $ inpatient_beds                                                               <dbl> …
## $ inpatient_beds_coverage                                                      <dbl> …
## $ inpatient_beds_used                                                          <dbl> …
## $ inpatient_beds_used_coverage                                                 <dbl> …
## $ inp                                                                          <dbl> …
## $ inpatient_beds_used_covid_coverage                                           <dbl> …
## $ previous_day_admission_adult_covid_confirmed                                 <dbl> …
## $ previous_day_admission_adult_covid_confirmed_coverage                        <dbl> …
## $ previous_day_admission_adult_covid_suspected                                 <dbl> …
## $ previous_day_admission_adult_covid_suspected_coverage                        <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed                             <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_coverage                    <dbl> …
## $ previous_day_admission_pediatric_covid_suspected                             <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_coverage                    <dbl> …
## $ staffed_adult_icu_bed_occupancy                                              <dbl> …
## $ staffed_adult_icu_bed_occupancy_coverage                                     <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid                     <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage            <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid                                   <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_coverage                          <dbl> …
## $ hosp_adult                                                                   <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage     <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid                            <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_coverage                   <dbl> …
## $ hosp_ped                                                                     <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid                        <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage               <dbl> …
## $ total_staffed_adult_icu_beds                                                 <dbl> …
## $ total_staffed_adult_icu_beds_coverage                                        <dbl> …
## $ inpatient_beds_utilization                                                   <dbl> …
## $ inpatient_beds_utilization_coverage                                          <dbl> …
## $ inpatient_beds_utilization_numerator                                         <dbl> …
## $ inpatient_beds_utilization_denominator                                       <dbl> …
## $ percent_of_inpatients_with_covid                                             <dbl> …
## $ percent_of_inpatients_with_covid_coverage                                    <dbl> …
## $ percent_of_inpatients_with_covid_numerator                                   <dbl> …
## $ percent_of_inpatients_with_covid_denominator                                 <dbl> …
## $ inpatient_bed_covid_utilization                                              <dbl> …
## $ inpatient_bed_covid_utilization_coverage                                     <dbl> …
## $ inpatient_bed_covid_utilization_numerator                                    <dbl> …
## $ inpatient_bed_covid_utilization_denominator                                  <dbl> …
## $ adult_icu_bed_covid_utilization                                              <dbl> …
## $ adult_icu_bed_covid_utilization_coverage                                     <dbl> …
## $ adult_icu_bed_covid_utilization_numerator                                    <dbl> …
## $ adult_icu_bed_covid_utilization_denominator                                  <dbl> …
## $ adult_icu_bed_utilization                                                    <dbl> …
## $ adult_icu_bed_utilization_coverage                                           <dbl> …
## $ adult_icu_bed_utilization_numerator                                          <dbl> …
## $ adult_icu_bed_utilization_denominator                                        <dbl> …
## $ geocoded_state                                                               <lgl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+`                           <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage`                  <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown                         <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+`                           <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_coverage`                  <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown                         <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_coverage                <dbl> …
## $ deaths_covid                                                                 <dbl> …
## $ deaths_covid_coverage                                                        <dbl> …
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses                   <dbl> …
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses                            <dbl> …
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses                 <dbl> …
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used               <dbl> …
## $ previous_week_therapeutic_b_bamlanivimab_courses_used                        <dbl> …
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used             <dbl> …
## $ icu_patients_confirmed_influenza                                             <dbl> …
## $ icu_patients_confirmed_influenza_coverage                                    <dbl> …
## $ previous_day_admission_influenza_confirmed                                   <dbl> …
## $ previous_day_admission_influenza_confirmed_coverage                          <dbl> …
## $ previous_day_deaths_covid_and_influenza                                      <dbl> …
## $ previous_day_deaths_covid_and_influenza_coverage                             <dbl> …
## $ previous_day_deaths_influenza                                                <dbl> …
## $ previous_day_deaths_influenza_coverage                                       <dbl> …
## $ total_patients_hospitalized_confirmed_influenza                              <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid                    <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage           <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_coverage                     <dbl> …
## $ all_pediatric_inpatient_bed_occupied                                         <dbl> …
## $ all_pediatric_inpatient_bed_occupied_coverage                                <dbl> …
## $ all_pediatric_inpatient_beds                                                 <dbl> …
## $ all_pediatric_inpatient_beds_coverage                                        <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4                         <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_coverage                <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17                       <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_coverage              <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11                        <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_coverage               <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown                     <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_coverage            <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid                               <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_coverage                      <dbl> …
## $ staffed_pediatric_icu_bed_occupancy                                          <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_coverage                                 <dbl> …
## $ total_staffed_pediatric_icu_beds                                             <dbl> …
## $ total_staffed_pediatric_icu_beds_coverage                                    <dbl> …
## Rows: 36184 Columns: 96
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (2): Date, Location
## dbl (94): MMWR_week, Distributed, Distributed_Janssen, Distributed_Moderna, ...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: Distributed_Novavax Administered_Novavax Series_Complete_Novavax
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 4
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 1 and at least 1%
## 
## [1] date     name     newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
## [1] state    name     newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
## 
## 
## 
## Raw file for vax:
## Rows: 36,184
## Columns: 96
## $ date                                   <date> 2022-08-31, 2022-08-31, 2022-0…
## $ MMWR_week                              <dbl> 35, 35, 35, 35, 35, 35, 35, 35,…
## $ state                                  <chr> "PW", "SD", "MA", "HI", "RI", "…
## $ Distributed                            <dbl> 47090, 2141765, 18793570, 38391…
## $ Distributed_Janssen                    <dbl> 3800, 92800, 626200, 124700, 90…
## $ Distributed_Moderna                    <dbl> 30000, 847500, 7168380, 1461820…
## $ Distributed_Pfizer                     <dbl> 13290, 1199665, 10993590, 22498…
## $ Distributed_Novavax                    <dbl> 0, 1800, 5400, 2800, 3200, 200,…
## $ Distributed_Unk_Manuf                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Dist_Per_100K                          <dbl> 218698, 242101, 272667, 271153,…
## $ Distributed_Per_100k_5Plus             <dbl> 231139, 260083, 287577, 288518,…
## $ Distributed_Per_100k_12Plus            <dbl> 252561, 290172, 312354, 316997,…
## $ Distributed_Per_100k_18Plus            <dbl> 283966, 320836, 339252, 344011,…
## $ Distributed_Per_100k_65Plus            <dbl> 2363960, 1410250, 1607210, 1430…
## $ vxa                                    <dbl> 49416, 1511407, 15773792, 31561…
## $ Administered_5Plus                     <dbl> 49373, 1507671, 15687231, 31448…
## $ Administered_12Plus                    <dbl> 46683, 1451767, 15028043, 30184…
## $ Administered_18Plus                    <dbl> 43018, 1359766, 14038745, 28178…
## $ Administered_65Plus                    <dbl> 5346, 453691, 3769576, 842523, …
## $ Administered_Janssen                   <dbl> 2357, 42334, 407539, 71355, 664…
## $ Administered_Moderna                   <dbl> 37794, 586034, 6193704, 1157104…
## $ Administered_Pfizer                    <dbl> 9098, 882891, 9171774, 1927032,…
## $ Administered_Novavax                   <dbl> 0, 0, 295, 10, 219, 1, 45, 25, …
## $ Administered_Unk_Manuf                 <dbl> 167, 148, 480, 697, 2259, 9, 25…
## $ Admin_Per_100k                         <dbl> 229500, 170846, 228854, 222915,…
## $ Admin_Per_100k_5Plus                   <dbl> 242345, 183083, 240044, 236338,…
## $ Admin_Per_100k_12Plus                  <dbl> 250378, 196689, 249770, 249232,…
## $ Admin_Per_100k_18Plus                  <dbl> 259410, 203693, 253421, 252497,…
## $ Admin_Per_100k_65Plus                  <dbl> 268373, 298734, 322370, 313850,…
## $ Recip_Administered                     <dbl> 49797, 1533994, 15858274, 31873…
## $ Administered_Dose1_Recip               <dbl> 20575, 700878, 6982383, 1266721…
## $ Administered_Dose1_Pop_Pct             <dbl> 95.0, 79.2, 95.0, 89.5, 95.0, 8…
## $ Administered_Dose1_Recip_5Plus         <dbl> 20547, 698199, 6928829, 1259039…
## $ Administered_Dose1_Recip_5PlusPop_Pct  <dbl> 95.0, 84.8, 95.0, 94.6, 95.0, 9…
## $ Administered_Dose1_Recip_12Plus        <dbl> 19119, 668658, 6593289, 1196906…
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 95.0, 90.6, 95.0, 95.0, 95.0, 9…
## $ Administered_Dose1_Recip_18Plus        <dbl> 17584, 622099, 6127404, 1107067…
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 95.0, 93.2, 95.0, 95.0, 95.0, 9…
## $ Administered_Dose1_Recip_65Plus        <dbl> 1876, 182192, 1462735, 275645, …
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 94.2, 95.0, 95.0, 95.0, 95.0, 8…
## $ vxc                                    <dbl> 18338, 563276, 5570460, 1128707…
## $ vxcpoppct                              <dbl> 85.2, 63.7, 80.8, 79.7, 84.9, 8…
## $ Series_Complete_5Plus                  <dbl> 18330, 563050, 5551263, 1126786…
## $ Series_Complete_5PlusPop_Pct           <dbl> 90.0, 68.4, 84.9, 84.7, 89.4, 9…
## $ Series_Complete_12Plus                 <dbl> 17241, 539812, 5278994, 1071938…
## $ Series_Complete_12PlusPop_Pct          <dbl> 92.5, 73.1, 87.7, 88.5, 92.3, 9…
## $ vxcgte18                               <dbl> 15791, 503622, 4896487, 990569,…
## $ vxcgte18pct                            <dbl> 95.0, 75.4, 88.4, 88.8, 93.0, 9…
## $ vxcgte65                               <dbl> 1811, 151094, 1165453, 252765, …
## $ vxcgte65pct                            <dbl> 90.9, 95.0, 95.0, 94.2, 95.0, 8…
## $ Series_Complete_Janssen                <dbl> 2361, 39918, 384642, 66056, 611…
## $ Series_Complete_Moderna                <dbl> 12724, 204498, 1968460, 371324,…
## $ Series_Complete_Pfizer                 <dbl> 3164, 318781, 3216940, 691089, …
## $ Series_Complete_Novavax                <dbl> 0, 2, 38, 1, 52, 1, 6, 6, 38, 2…
## $ Series_Complete_Unk_Manuf              <dbl> 82, 70, 290, 215, 602, 3, 591, …
## $ Series_Complete_Janssen_5Plus          <dbl> 2361, 39914, 384637, 66028, 611…
## $ Series_Complete_Moderna_5Plus          <dbl> 12724, 204289, 1955713, 370535,…
## $ Series_Complete_Pfizer_5Plus           <dbl> 3163, 318775, 3210586, 690007, …
## $ Series_Complete_Unk_Manuf_5Plus        <dbl> 82, 70, 289, 215, 587, 3, 591, …
## $ Series_Complete_Janssen_12Plus         <dbl> 2361, 39912, 384612, 66026, 611…
## $ Series_Complete_Moderna_12Plus         <dbl> 12724, 204257, 1953836, 370413,…
## $ Series_Complete_Pfizer_12Plus          <dbl> 2074, 295572, 2940222, 635307, …
## $ Series_Complete_Unk_Manuf_12Plus       <dbl> 82, 69, 286, 191, 572, 3, 588, …
## $ Series_Complete_Janssen_18Plus         <dbl> 2361, 39882, 383306, 65839, 611…
## $ Series_Complete_Moderna_18Plus         <dbl> 12723, 204149, 1948279, 369555,…
## $ Series_Complete_Pfizer_18Plus          <dbl> 625, 259525, 2564603, 555015, 4…
## $ Series_Complete_Unk_Manuf_18Plus       <dbl> 82, 64, 262, 159, 543, 3, 574, …
## $ Series_Complete_Janssen_65Plus         <dbl> 227, 5079, 74665, 11821, 6832, …
## $ Series_Complete_Moderna_65Plus         <dbl> 1542, 74263, 531381, 111196, 86…
## $ Series_Complete_Pfizer_65Plus          <dbl> 40, 71727, 559321, 129727, 1005…
## $ Series_Complete_Unk_Manuf_65Plus       <dbl> 2, 25, 80, 21, 162, 0, 263, 69,…
## $ Additional_Doses                       <dbl> 12048, 248903, 2987198, 646528,…
## $ Additional_Doses_Vax_Pct               <dbl> 65.7, 44.2, 53.6, 57.3, 56.1, 5…
## $ Additional_Doses_5Plus                 <dbl> 12048, 248900, 2987162, 646515,…
## $ Additional_Doses_5Plus_Vax_Pct         <dbl> 65.7, 44.2, 53.8, 57.4, 56.2, 5…
## $ Additional_Doses_12Plus                <dbl> 11872, 246180, 2938665, 637193,…
## $ Additional_Doses_12Plus_Vax_Pct        <dbl> 68.9, 45.6, 55.7, 59.4, 58.4, 5…
## $ Additional_Doses_18Plus                <dbl> 11181, 236970, 2791202, 606621,…
## $ Additional_Doses_18Plus_Vax_Pct        <dbl> 70.8, 47.1, 57.0, 61.2, 60.1, 5…
## $ Additional_Doses_50Plus                <dbl> 4815, 163923, 1630617, 376685, …
## $ Additional_Doses_50Plus_Vax_Pct        <dbl> 80.1, 58.2, 66.0, 75.0, 71.4, 7…
## $ Additional_Doses_65Plus                <dbl> 1575, 98849, 840257, 208155, 15…
## $ Additional_Doses_65Plus_Vax_Pct        <dbl> 87.0, 65.4, 72.1, 82.4, 79.0, 7…
## $ Additional_Doses_Moderna               <dbl> 10870, 109000, 1349812, 272149,…
## $ Additional_Doses_Pfizer                <dbl> 1176, 136782, 1609614, 367759, …
## $ Additional_Doses_Janssen               <dbl> 2, 3093, 27721, 6500, 5296, 217…
## $ Additional_Doses_Unk_Manuf             <dbl> 0, 26, 45, 118, 129, 0, 438, 78…
## $ Second_Booster                         <dbl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Second_Booster_50Plus                  <dbl> 1126, 53725, 590595, 170399, 10…
## $ Second_Booster_50Plus_Vax_Pct          <dbl> 23.4, 32.8, 36.2, 45.2, 34.4, 1…
## $ Second_Booster_65Plus                  <dbl> 383, 39382, 379347, 111314, 667…
## $ Second_Booster_65Plus_Vax_Pct          <dbl> 24.3, 39.8, 45.1, 53.5, 43.5, 2…
## $ Second_Booster_Janssen                 <dbl> 0, 27, 253, 120, 151, 1, 119, 2…
## $ Second_Booster_Moderna                 <dbl> 1148, 24919, 309097, 87335, 498…
## $ Second_Booster_Pfizer                  <dbl> 22, 30731, 314869, 91565, 57827…
## $ Second_Booster_Unk_Manuf               <dbl> 0, 2, 10, 15, 53, 0, 80, 28, 21…
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 6
##   isType tot_cases tot_deaths     new_cases    new_deaths         n
##   <chr>      <dbl>      <dbl>         <dbl>         <dbl>     <dbl>
## 1 before  3.53e+10    5.16e+8 93993694      1025546       56522    
## 2 after   3.51e+10    5.14e+8 92929415      1019927       48858    
## 3 pctchg  6.80e- 3    4.57e-3        0.0113       0.00548     0.136
## 
## 
## Processed for cdcDaily:
## Rows: 48,858
## Columns: 6
## $ date       <date> 2021-03-11, 2021-12-01, 2021-09-01, 2021-03-08, 2021-09-17…
## $ state      <chr> "KS", "ND", "ND", "IN", "FL", "TN", "IA", "SD", "HI", "MA",…
## $ tot_cases  <dbl> 297229, 163565, 118491, 668765, 3510205, 64885, 20015, 1226…
## $ tot_deaths <dbl> 4851, 1907, 1562, 12710, 56036, 749, 561, 1967, 17, 17818, …
## $ new_cases  <dbl> 0, 589, 536, 487, 9979, 1816, 317, 28, 8, 451, 1040, 133, 0…
## $ new_deaths <dbl> 0, 9, 1, 7, 294, 8, 12, 0, 0, 5, 4, 0, 0, 5, 1, 3, 0, 0, 22…
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 5
##   isType     inp hosp_adult     hosp_ped          n
##   <chr>    <dbl>      <dbl>        <dbl>      <dbl>
## 1 before 5.11e+7    4.45e+7 1229807      49367     
## 2 after  5.09e+7    4.43e+7 1205197      47181     
## 3 pctchg 5.37e-3    5.13e-3       0.0200     0.0443
## 
## 
## Processed for cdcHosp:
## Rows: 47,181
## Columns: 5
## $ date       <date> 2021-01-06, 2021-01-06, 2020-12-31, 2020-12-30, 2020-12-29…
## $ state      <chr> "MA", "OR", "SD", "RI", "OR", "OH", "LA", "WV", "VT", "WY",…
## $ inp        <dbl> 2232, 583, 282, 471, 626, 5534, 1461, 242, 0, 71, 1, 91, 49…
## $ hosp_adult <dbl> 2209, 568, 280, 469, 615, 5443, 1449, 241, 0, 70, 1, 90, 48…
## $ hosp_ped   <dbl> 23, 15, 2, 2, 11, 91, 12, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, …
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 9
##   isType      vxa      vxc   vxcpoppct vxcgte65 vxcgt…¹ vxcgte18 vxcgt…²       n
##   <chr>     <dbl>    <dbl>       <dbl>    <dbl>   <dbl>    <dbl>   <dbl>   <dbl>
## 1 before 4.14e+11 1.70e+11 1511640.    4.31e+10 2.24e+6 1.57e+11 1.78e+6 3.62e+4
## 2 after  2.00e+11 8.22e+10 1265373.    2.09e+10 1.98e+6 7.61e+10 1.51e+6 2.86e+4
## 3 pctchg 5.18e- 1 5.16e- 1       0.163 5.16e- 1 1.14e-1 5.16e- 1 1.54e-1 2.09e-1
## # … with abbreviated variable names ¹​vxcgte65pct, ²​vxcgte18pct
## 
## 
## Processed for vax:
## Rows: 28,611
## Columns: 9
## $ date        <date> 2022-08-31, 2022-08-31, 2022-08-31, 2022-08-31, 2022-08-3…
## $ state       <chr> "SD", "MA", "HI", "RI", "MT", "WY", "LA", "KS", "IN", "MS"…
## $ vxa         <dbl> 1511407, 15773792, 3156198, 2353711, 1675440, 778457, 6536…
## $ vxc         <dbl> 563276, 5570460, 1128707, 899544, 618143, 300240, 2526855,…
## $ vxcpoppct   <dbl> 63.7, 80.8, 79.7, 84.9, 57.8, 51.9, 54.4, 63.3, 56.8, 53.0…
## $ vxcgte65    <dbl> 151094, 1165453, 252765, 194268, 180238, 84688, 642150, 44…
## $ vxcgte65pct <dbl> 95.0, 95.0, 94.2, 95.0, 87.3, 85.4, 86.7, 94.6, 88.5, 85.2…
## $ vxcgte18    <dbl> 503622, 4896487, 990569, 794734, 562722, 275699, 2323648, …
## $ vxcgte18pct <dbl> 75.4, 88.4, 88.8, 93.0, 67.0, 62.0, 65.2, 74.2, 67.0, 63.3…
## 
## Integrated per capita data file:
## Rows: 49,071
## Columns: 34
## $ date        <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0…
## $ state       <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"…
## $ tot_cases   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tot_deaths  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_cases   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_deaths  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ inp         <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, …
## $ hosp_adult  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hosp_ped    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxa         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxc         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpoppct   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm         <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA…
## $ ahpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ ahpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm7      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm7      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in Proj4 definition

saveToRDS(cdc_daily_220907, ovrWriteError=FALSE)

The function is run to download and process the latest hospitalization data:

# Run for latest data, save as RDS
indivHosp_20220907 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20220907.csv")
## 
## File ./RInputFiles/Coronavirus/HHS_Hospital_20220907.csv already exists
## File will not be downloaded since ovrWrite is not TRUE
## Rows: 269456 Columns: 128
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr   (11): hospital_pk, state, ccn, hospital_name, address, city, zip, hosp...
## dbl  (114): total_beds_7_day_avg, all_adult_hospital_beds_7_day_avg, all_adu...
## lgl    (2): is_metro_micro, is_corrected
## date   (1): collection_week
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 269,456
## Columns: 128
## $ hospital_pk                                                                        <chr> …
## $ collection_week                                                                    <date> …
## $ state                                                                              <chr> …
## $ ccn                                                                                <chr> …
## $ hospital_name                                                                      <chr> …
## $ address                                                                            <chr> …
## $ city                                                                               <chr> …
## $ zip                                                                                <chr> …
## $ hospital_subtype                                                                   <chr> …
## $ fips_code                                                                          <chr> …
## $ is_metro_micro                                                                     <lgl> …
## $ total_beds_7_day_avg                                                               <dbl> …
## $ all_adult_hospital_beds_7_day_avg                                                  <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_avg                                        <dbl> …
## $ inpatient_beds_used_7_day_avg                                                      <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg                                <dbl> …
## $ inpatient_beds_used_covid_7_day_avg                                                <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg          <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg                        <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg      <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg                    <dbl> …
## $ inpatient_beds_7_day_avg                                                           <dbl> …
## $ total_icu_beds_7_day_avg                                                           <dbl> …
## $ total_staffed_adult_icu_beds_7_day_avg                                             <dbl> …
## $ icu_beds_used_7_day_avg                                                            <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_avg                                          <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg                 <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg                               <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg                          <dbl> …
## $ icu_patients_confirmed_influenza_7_day_avg                                         <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg                <dbl> …
## $ total_beds_7_day_sum                                                               <dbl> …
## $ all_adult_hospital_beds_7_day_sum                                                  <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_sum                                        <dbl> …
## $ inpatient_beds_used_7_day_sum                                                      <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum                                <dbl> …
## $ inpatient_beds_used_covid_7_day_sum                                                <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum          <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum                        <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum      <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum                    <dbl> …
## $ inpatient_beds_7_day_sum                                                           <dbl> …
## $ total_icu_beds_7_day_sum                                                           <dbl> …
## $ total_staffed_adult_icu_beds_7_day_sum                                             <dbl> …
## $ icu_beds_used_7_day_sum                                                            <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_sum                                          <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum                 <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum                               <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum                          <dbl> …
## $ icu_patients_confirmed_influenza_7_day_sum                                         <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum                <dbl> …
## $ total_beds_7_day_coverage                                                          <dbl> …
## $ all_adult_hospital_beds_7_day_coverage                                             <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_coverage                                   <dbl> …
## $ inpatient_beds_used_7_day_coverage                                                 <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage                           <dbl> …
## $ inpatient_beds_used_covid_7_day_coverage                                           <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage     <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage                   <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage               <dbl> …
## $ inpatient_beds_7_day_coverage                                                      <dbl> …
## $ total_icu_beds_7_day_coverage                                                      <dbl> …
## $ total_staffed_adult_icu_beds_7_day_coverage                                        <dbl> …
## $ icu_beds_used_7_day_coverage                                                       <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_coverage                                     <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage            <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage                          <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage                     <dbl> …
## $ icu_patients_confirmed_influenza_7_day_coverage                                    <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage           <dbl> …
## $ previous_day_admission_adult_covid_confirmed_7_day_sum                             <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum`                       <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum                     <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum                         <dbl> …
## $ previous_day_covid_ED_visits_7_day_sum                                             <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_sum                             <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum`                       <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum                     <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum                         <dbl> …
## $ previous_day_total_ED_visits_7_day_sum                                             <dbl> …
## $ previous_day_admission_influenza_confirmed_7_day_sum                               <dbl> …
## $ geocoded_hospital_address                                                          <chr> …
## $ hhs_ids                                                                            <chr> …
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage                        <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage                    <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_coverage                        <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage                    <dbl> …
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day                  <dbl> …
## $ total_personnel_covid_vaccinated_doses_none_7_day                                  <dbl> …
## $ total_personnel_covid_vaccinated_doses_one_7_day                                   <dbl> …
## $ total_personnel_covid_vaccinated_doses_all_7_day                                   <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_one_7_day                            <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_all_7_day                            <dbl> …
## $ is_corrected                                                                       <lgl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_avg                                     <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_coverage                                <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_sum                                     <dbl> …
## $ all_pediatric_inpatient_beds_7_day_avg                                             <dbl> …
## $ all_pediatric_inpatient_beds_7_day_coverage                                        <dbl> …
## $ all_pediatric_inpatient_beds_7_day_sum                                             <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum                     <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum                   <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum                    <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum                 <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_avg                           <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage                      <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_sum                           <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_avg                                      <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_coverage                                 <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_sum                                      <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_avg                                         <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_coverage                                    <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_sum                                         <dbl> …
## 
## Hospital Subtype Counts:
## # A tibble: 4 × 2
##   hospital_subtype               n
##   <chr>                      <int>
## 1 Childrens Hospitals         5077
## 2 Critical Access Hospitals  72331
## 3 Long Term                  18519
## 4 Short Term                173529
## 
## Records other than 50 states and DC
## # A tibble: 5 × 2
##   state     n
##   <chr> <int>
## 1 AS       54
## 2 GU      106
## 3 MP       46
## 4 PR     2864
## 5 VI      106
## 
## Record types for key metrics
## # A tibble: 10 × 5
##    name                                              `NA` Posit…¹ Value…²  Total
##    <chr>                                            <int>   <int>   <int>  <int>
##  1 all_adult_hospital_beds_7_day_avg                64869  204079     508 269456
##  2 all_adult_hospital_inpatient_bed_occupied_7_day…   143  247227   22086 269456
##  3 icu_beds_used_7_day_avg                             64  237310   32082 269456
##  4 inpatient_beds_7_day_avg                            67  268366    1023 269456
##  5 inpatient_beds_used_7_day_avg                       51  248042   21363 269456
##  6 inpatient_beds_used_covid_7_day_avg                 32  182000   87424 269456
##  7 staffed_icu_adult_patients_confirmed_and_suspec…   162  184312   84982 269456
##  8 total_adult_patients_hospitalized_confirmed_and…   121  181944   87391 269456
##  9 total_beds_7_day_avg                             63149  206009     298 269456
## 10 total_icu_beds_7_day_avg                            74  255402   13980 269456
## # … with abbreviated variable names ¹​Positive, ²​`Value -999999`
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

saveToRDS(indivHosp_20220907, ovrWriteError=FALSE)
## 
## File already exists: ./RInputFiles/Coronavirus/indivHosp_20220907.RDS 
## 
## Not replacing the existing file since ovrWrite=FALSE
## NULL

Post-processing is run, including hospital summaries:

# Create pivoted burden data
burdenPivotList_220907 <- postProcessCDCDaily(cdc_daily_220907, 
                                              dataThruLabel="Aug 2022", 
                                              keyDatesBurden=c("2022-08-31", "2022-02-28", 
                                                               "2021-08-31", "2021-02-28"
                                                               ),
                                              keyDatesVaccine=c("2022-08-31", "2022-03-31", 
                                                                "2021-10-31", "2021-05-31"
                                                                ), 
                                              returnData=TRUE
                                              )
## Joining, by = "state"
## 
## *** File has been checked for uniqueness by: state date name
## Warning: Removed 24 row(s) containing missing values (geom_path).

## Warning: Removed 24 rows containing missing values (position_stack).

## Warning: Removed 24 rows containing missing values (position_stack).

## Warning: Removed 9 row(s) containing missing values (geom_path).

# Create hospitalized per capita data
hospPerCap_220907 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"), 
                                      lst=burdenPivotList_220907, 
                                      popVar="pop2019", 
                                      excludeState=c(), 
                                      cumStartDate="2020-07-15"
                                      )
## Warning: Removed 18 row(s) containing missing values (geom_path).

burdenPivotList_220907$hospAge %>%
    group_by(adultPed, confSusp, age, name) %>%
    summarize(value=sum(value, na.rm=TRUE), n=n(), .groups="drop")
## # A tibble: 18 × 6
##    adultPed confSusp  age   name                                     value     n
##    <chr>    <chr>     <chr> <chr>                                    <dbl> <int>
##  1 adult    confirmed 0-19  previous_day_admission_adult_covid_con… 4.70e4 49367
##  2 adult    confirmed 20-29 previous_day_admission_adult_covid_con… 2.86e5 49367
##  3 adult    confirmed 30-39 previous_day_admission_adult_covid_con… 4.12e5 49367
##  4 adult    confirmed 40-49 previous_day_admission_adult_covid_con… 4.96e5 49367
##  5 adult    confirmed 50-59 previous_day_admission_adult_covid_con… 7.91e5 49367
##  6 adult    confirmed 60-69 previous_day_admission_adult_covid_con… 1.04e6 49367
##  7 adult    confirmed 70-79 previous_day_admission_adult_covid_con… 1.04e6 49367
##  8 adult    confirmed 80+   previous_day_admission_adult_covid_con… 9.32e5 49367
##  9 adult    suspected 0-19  previous_day_admission_adult_covid_sus… 3.83e4 49367
## 10 adult    suspected 20-29 previous_day_admission_adult_covid_sus… 2.56e5 49367
## 11 adult    suspected 30-39 previous_day_admission_adult_covid_sus… 3.35e5 49367
## 12 adult    suspected 40-49 previous_day_admission_adult_covid_sus… 3.39e5 49367
## 13 adult    suspected 50-59 previous_day_admission_adult_covid_sus… 5.37e5 49367
## 14 adult    suspected 60-69 previous_day_admission_adult_covid_sus… 7.38e5 49367
## 15 adult    suspected 70-79 previous_day_admission_adult_covid_sus… 7.19e5 49367
## 16 adult    suspected 80+   previous_day_admission_adult_covid_sus… 6.55e5 49367
## 17 ped      confirmed 0-19  previous_day_admission_pediatric_covid… 1.67e5 49367
## 18 ped      suspected 0-19  previous_day_admission_pediatric_covid… 3.74e5 49367
saveToRDS(burdenPivotList_220907, ovrWriteError=FALSE)
saveToRDS(hospPerCap_220907, ovrWriteError=FALSE)

Peaks and valleys of key metrics are also updated:

peakValleyCDCDaily(cdc_daily_220907)
## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 20 row(s) containing missing values (geom_path).

## Warning: Removed 20 row(s) containing missing values (geom_path).

## # A tibble: 7,740 × 8
##    date       state   vxa   vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
##    <date>     <chr> <dbl> <dbl> <lgl>      <lgl>      <lgl>        <lgl>       
##  1 2020-12-01 CA       NA    NA FALSE      FALSE      FALSE        FALSE       
##  2 2020-12-01 FL       NA    NA FALSE      FALSE      FALSE        FALSE       
##  3 2020-12-01 GA       NA    NA FALSE      FALSE      FALSE        FALSE       
##  4 2020-12-01 IL       NA    NA FALSE      FALSE      FALSE        FALSE       
##  5 2020-12-01 MI       NA    NA FALSE      FALSE      FALSE        FALSE       
##  6 2020-12-01 NC       NA    NA FALSE      FALSE      FALSE        FALSE       
##  7 2020-12-01 NJ       NA    NA FALSE      FALSE      FALSE        FALSE       
##  8 2020-12-01 NY       NA    NA FALSE      FALSE      FALSE        FALSE       
##  9 2020-12-01 OH       NA    NA FALSE      FALSE      FALSE        FALSE       
## 10 2020-12-01 PA       NA    NA FALSE      FALSE      FALSE        FALSE       
## # … with 7,730 more rows
## # ℹ Use `print(n = ...)` to see more rows

Hospital capacity is updated using a mix of old data (for 2021) and new data:

identical(names(indivHosp_20220907), names(readFromRDS("indivHosp_20220704")))
## [1] TRUE
modHospData <- bind_rows(filter(readFromRDS("indivHosp_20220704"), lubridate::year(collection_week)<2022), 
                         filter(indivHosp_20220907, lubridate::year(collection_week)>=2022), 
                         .id="src"
                         )
updated_modStateHosp_20220907 <- hospitalCapacityCDCDaily(modHospData, 
                                                          plotSub="Aug 2020 to Aug 2022\nOld data used pre-2022"
                                                          )

Data availability by source and time is assessed:

# Temporary function to aggregate data
tempCounter <- function(df) {
    df %>%
        select(hospital_pk, collection_week, all_of(names(hhsMapper))) %>%
        colRenamer(vecRename=hhsMapper) %>%
        pivot_longer(-c(hospital_pk, collection_week)) %>%
        filter(!is.na(value), value>0) %>%
        count(collection_week, name)
}

dfTemp <- bind_rows(tempCounter(indivHosp_20220907), tempCounter(readFromRDS("indivHosp_20220704")), .id="src")

dfTemp %>%
    select(collection_week, name) %>%
    unique() %>%
    bind_rows(., ., .id="src")  %>%
    full_join(dfTemp, by=c("src", "collection_week", "name")) %>%
    mutate(src=c("1"="SEP-2022", "2"="JUL-2022")[src]) %>%
    mutate(n=ifelse(is.na(n), 0, n)) %>%
    ggplot(aes(x=collection_week, y=n)) +
    geom_line(aes(group=src, color=src)) + 
    facet_wrap(~name) + 
    labs(title="Number of hospitals in US reporting >0 on metric by week", x=NULL, y="# Hospitals Reporting > 0") + 
    scale_color_discrete("Data Source:")

dfTemp %>%
    select(collection_week, name) %>%
    unique() %>%
    bind_rows(., ., .id="src")  %>%
    full_join(dfTemp, by=c("src", "collection_week", "name")) %>%
    mutate(src=c("1"="SEP-2022", "2"="JUL-2022")[src]) %>%
    mutate(n=ifelse(is.na(n), 0, n)) %>% 
    group_by(collection_week, name) %>% 
    summarize(delta=sum(ifelse(src=="SEP-2022", n, 0)-ifelse(src!="SEP-2022", n, 0)), .groups="drop") %>%
    ggplot(aes(x=collection_week, y=delta)) +
    geom_line(aes(color=case_when(delta>=0 ~ "darkgreen", TRUE ~ "red"))) +
    geom_hline(yintercept=0, lty=2) +
    geom_vline(xintercept=c(as.Date("2021-08-20"), as.Date("2022-06-24")), lty=2) +
    scale_color_identity(NULL) +
    facet_wrap(~name) + 
    labs(title="Delta in Number of hospitals in US reporting >0 on metric by week", 
         subtitle="Trend break dashed lines at 2021-08-20 and 2022-06-24",
         x=NULL, 
         y="Delta in # Hospitals Reporting > 0"
         )

The process is converted to functional form:

multiSourceDataCombine <- function(lst, timeVec, keyVar="collection_week", idName="src") {
    
    # FUNCTION ARGUMENTS:
    # lst: list of data frames to be combined
    # timeVec: vector of time cut points (data before timeVec[1] taken from lst[[1]], etc.)
    # keyVar: variable describing time in the data
    # idName: name of column for .id when files combined
    
    # Check list lengths
    if(length(lst)==0) {
        cat("\nEmpty list passed, returning 0x0 tibble\n")
        return(tibble::tibble())
    } else if (length(lst)==1) {
        cat("\nList of length 1 passed, returning item in list as-is")
        return(lst[[1]])
    }
    
    # Check that timeVec matches
    if(length(lst) != length(timeVec) + 1) stop("\nMismatch of lst and timeVec\n")
    
    # Check that all data frames have the same column names in the same order
    vecNames <- names(lst[[1]])
    for(n in 2:length(lst)) if(!isTRUE(identical(names(lst[[n]]), vecNames))) stop("\nName mismatch in files\n")
    
    # Combine data
    bind_rows(lst, .id=idName) %>%
        mutate(srcNum=as.integer(get(idName)), 
               dateMin=ifelse(srcNum==1, NA, timeVec[srcNum-1]), 
               dateMax=ifelse(srcNum==max(srcNum), NA, timeVec[srcNum])
               ) %>%
        filter(is.na(dateMin) | get(keyVar) >= dateMin, 
               is.na(dateMax) | get(keyVar) < dateMax
               ) %>%
        select(-srcNum, -dateMin, -dateMax)
    
}

# Create modified hospital data
multiSourceHosp_20220902 <- multiSourceDataCombine(list(readFromRDS("indivHosp_20220704"), 
                                                        indivHosp_20220907
                                                        ), 
                                                   timeVec=as.Date("2022-01-01")
                                                   )

# Confirm that function produces expected output
multiSourceHosp_20220902 %>%
    select(-src) %>%
    identical(modHospData %>% select(-src))
## [1] TRUE

The updated hospital data are then plotted:

# Run hospital plots
modStateHosp_20220902 <- hospitalCapacityCDCDaily(multiSourceHosp_20220902, 
                                                  plotSub="Aug 2020 to Aug 2022\nOld data used pre-2022"
                                                  )

Data Refreshes

Data From 2022-10-02

The latest CDC case, hospitalization, and death data are downloaded and processed:

readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_221002.csv", 
                 "cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_221002.csv", 
                 "vax"="./RInputFiles/Coronavirus/vaxData_downloaded_221002.csv"
                 )
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220907")$dfRaw$cdcDaily, 
                    "cdcHosp"=readFromRDS("cdc_daily_220907")$dfRaw$cdcHosp, 
                    "vax"=readFromRDS("cdc_daily_220907")$dfRaw$vax
                    )

cdc_daily_221002 <- readRunCDCDaily(thruLabel="Sep 30, 2022", 
                                    downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x), 
                                    readFrom=readList,
                                    compareFile=compareList, 
                                    writeLog=NULL, 
                                    useClusters=readFromRDS("cdc_daily_210528")$useClusters, 
                                    weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7", 
                                                       "vxcpm7", "vxcgte65pct"
                                                       ),
                                    skipAssessmentPlots=FALSE, 
                                    brewPalette="Paired"
                                    )
## Rows: 58980 Columns: 15
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (5): submission_date, state, created_at, consent_cases, consent_deaths
## dbl (10): tot_cases, conf_cases, prob_cases, new_case, pnew_case, tot_death,...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 25
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 5 and at least 5%
## 
##          date       name newValue refValue absDelta   pctDelta
## 1  2022-09-04 new_deaths      109       20       89 1.37984496
## 2  2022-09-03 new_deaths      116       24       92 1.31428571
## 3  2022-08-27 new_deaths      163       91       72 0.56692913
## 4  2022-09-05 new_deaths      106       60       46 0.55421687
## 5  2022-08-28 new_deaths      117       87       30 0.29411765
## 6  2022-08-20 new_deaths      139      111       28 0.22400000
## 7  2021-03-03 new_deaths     1899     1547      352 0.20429483
## 8  2022-08-06 new_deaths      174      142       32 0.20253165
## 9  2022-08-13 new_deaths      137      112       25 0.20080321
## 10 2020-09-14 new_deaths      376      453       77 0.18576598
## 11 2021-03-02 new_deaths     1134     1357      223 0.17904456
## 12 2022-08-21 new_deaths      122      102       20 0.17857143
## 13 2022-07-02 new_deaths      146      124       22 0.16296296
## 14 2022-09-01 new_deaths      499      428       71 0.15318231
## 15 2022-07-30 new_deaths      151      130       21 0.14946619
## 16 2022-07-04 new_deaths      120      138       18 0.13953488
## 17 2021-03-18 new_deaths      961      843      118 0.13082040
## 18 2022-06-27 new_deaths      259      295       36 0.12996390
## 19 2022-09-02 new_deaths      509      453       56 0.11642412
## 20 2022-08-14 new_deaths      115      103       12 0.11009174
## 21 2022-05-30 new_deaths       78       86        8 0.09756098
## 22 2022-04-25 new_deaths      181      199       18 0.09473684
## 23 2021-03-19 new_deaths     1080     1182      102 0.09018568
## 24 2022-07-09 new_deaths      120      110       10 0.08695652
## 25 2022-08-25 new_deaths      536      492       44 0.08560311
## 26 2022-08-30 new_deaths      633      582       51 0.08395062
## 27 2022-04-04 new_deaths      325      353       28 0.08259587
## 28 2022-08-26 new_deaths      674      621       53 0.08185328
## 29 2022-07-16 new_deaths      163      151       12 0.07643312
## 30 2022-08-01 new_deaths      402      433       31 0.07425150
## 31 2021-10-11 new_deaths      983      915       68 0.07165437
## 32 2022-06-20 new_deaths      140      150       10 0.06896552
## 33 2021-12-28 new_deaths     2334     2185      149 0.06594379
## 34 2022-08-31 new_deaths      778      830       52 0.06467662
## 35 2020-09-15 new_deaths      827      777       50 0.06234414
## 36 2020-09-21 new_deaths      547      580       33 0.05856256
## 37 2021-02-28 new_deaths     1027     1088       61 0.05768322
## 38 2022-08-19 new_deaths      622      588       34 0.05619835
## 39 2021-03-01 new_deaths     1333     1262       71 0.05472062
## 40 2021-06-28 new_deaths      183      193       10 0.05319149
## 41 2021-10-04 new_deaths     1298     1234       64 0.05055292
## 42 2022-09-03  new_cases    26751    14942    11809 0.56647399
## 43 2022-09-04  new_cases    26642    18143     8499 0.37954672
## 44 2022-08-27  new_cases    31520    27723     3797 0.12818392
## 45 2022-08-20  new_cases    31149    27757     3392 0.11516654
## 46 2022-07-04  new_cases    46367    50779     4412 0.09083236
## 47 2022-08-06  new_cases    36583    33586     2997 0.08542234
## 48 2022-08-28  new_cases    29299    26941     2358 0.08385491
## 49 2022-08-13  new_cases    33397    30736     2661 0.08298380
## 50 2022-07-30  new_cases    41625    38338     3287 0.08221302
## 51 2022-08-29  new_cases    80569    87383     6814 0.08114223
## 52 2022-08-14  new_cases    25021    23110     1911 0.07940828
## 53 2022-08-21  new_cases    30913    28587     2326 0.07818487
## 54 2022-07-11  new_cases   115394   124421     9027 0.07528303
## 55 2022-09-01  new_cases   106616    99807     6809 0.06597133
## 56 2021-10-04  new_cases    92025    86161     5864 0.06581886
## 57 2021-09-20  new_cases    98599    92534     6065 0.06346366
## 58 2022-07-09  new_cases    58075    54584     3491 0.06197463
## 59 2022-07-23  new_cases    56226    52972     3254 0.05959816
## 60 2022-09-02  new_cases   107474   101329     6145 0.05885931
## 61 2022-07-02  new_cases    51723    48818     2905 0.05778737
## 62 2022-08-07  new_cases    37208    35135     2073 0.05731031
## 63 2021-09-06  new_cases   115878   109526     6352 0.05636102
## 64 2022-07-18  new_cases   103308   109131     5823 0.05482044
## 65 2021-09-13  new_cases   119108   112822     6286 0.05420601
## 66 2022-07-31  new_cases    42016    39803     2213 0.05409501
## 67 2021-09-27  new_cases   107419   101840     5579 0.05332148
## 68 2022-07-16  new_cases    58177    55197     2980 0.05256937

## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
##    state       name  newValue  refValue absDelta    pctDelta
## 1     VA tot_deaths   9582222   9605584    23362 0.002435088
## 2     KY tot_deaths   7519333   7506070    13263 0.001765410
## 3     VA  tot_cases 726116885 728276276  2159391 0.002969474
## 4     KY new_deaths     16974     16757      217 0.012866503
## 5     AL new_deaths     20360     20203      157 0.007741045
## 6     VA new_deaths     21479     21334      145 0.006773644
## 7     FL new_deaths     80737     80209      528 0.006561207
## 8     KS new_deaths      9001      9054       53 0.005870950
## 9     NC new_deaths     26416     26338       78 0.002957122
## 10    OK new_deaths     13708     13746       38 0.002768267
## 11    SC new_deaths     18313     18263       50 0.002734033
## 12    SC  new_cases   1688432   1674281    14151 0.008416419
## 13    KY  new_cases   1559169   1546719    12450 0.008017031
## 14    NC  new_cases   3143278   3123308    19970 0.006373486
## 15    CO  new_cases   1637839   1634396     3443 0.002104372
## 16    WA  new_cases   1789708   1787212     2496 0.001395614
## 
## 
## 
## Raw file for cdcDaily:
## Rows: 58,980
## Columns: 15
## $ date           <date> 2021-03-11, 2021-12-01, 2020-04-07, 2020-04-08, 2020-0…
## $ state          <chr> "KS", "ND", "AS", "AR", "AR", "ND", "IN", "AR", "NY", "…
## $ tot_cases      <dbl> 297229, 163565, 0, 1071, 0, 118491, 668765, 56199, 1882…
## $ conf_cases     <dbl> 241035, 135705, NA, NA, NA, 107475, NA, NA, NA, 144788,…
## $ prob_cases     <dbl> 56194, 27860, NA, NA, NA, 11016, NA, NA, NA, 29179, 125…
## $ new_cases      <dbl> 0, 589, 0, 78, 0, 536, 487, 547, 318, 667, 154, 1509, 0…
## $ pnew_case      <dbl> 0, 220, NA, NA, NA, 66, 0, 0, 0, 274, 43, 0, 0, 616, 0,…
## $ tot_deaths     <dbl> 4851, 1907, 0, 18, 0, 1562, 12710, 674, 8822, 2911, 115…
## $ conf_death     <dbl> NA, NA, NA, NA, NA, NA, 12315, NA, NA, 2482, 9205, NA, …
## $ prob_death     <dbl> NA, NA, NA, NA, NA, NA, 395, NA, NA, 429, 2325, NA, 152…
## $ new_deaths     <dbl> 0, 9, 0, 0, 0, 1, 7, 11, 2, 8, 5, 6, 0, 100, 0, 5, 0, 0…
## $ pnew_death     <dbl> 0, 0, NA, NA, NA, 0, 2, 0, 0, 3, 1, 0, 0, 34, 0, 0, 0, …
## $ created_at     <chr> "03/12/2021 03:20:13 PM", "12/02/2021 02:35:20 PM", "04…
## $ consent_cases  <chr> "Agree", "Agree", NA, "Not agree", "Not agree", "Agree"…
## $ consent_deaths <chr> "N/A", "Not agree", NA, "Not agree", "Not agree", "Not …
## Rows: 50717 Columns: 135
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr    (1): state
## dbl  (132): critical_staffing_shortage_today_yes, critical_staffing_shortage...
## lgl    (1): geocoded_state
## date   (1): date
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 25
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 5 and at least 5%
## 
##         date       name newValue refValue absDelta  pctDelta
## 1 2022-09-06        inp    35899    31213     4686 0.1396472
## 2 2020-07-25   hosp_ped     4543     3964      579 0.1361232
## 3 2022-09-06   hosp_ped     1651     1543      108 0.0676268
## 4 2022-09-06 hosp_adult    34457    29670     4787 0.1492975

## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
##    state       name newValue refValue absDelta    pctDelta
## 1     KY        inp   816280   813897     2383 0.002923609
## 2     NC        inp  1500803  1499030     1773 0.001182066
## 3     ME   hosp_ped     2525     2573       48 0.018830914
## 4     SC   hosp_ped     9677     9528      149 0.015516793
## 5     WV   hosp_ped     6031     6124       93 0.015302345
## 6     DE   hosp_ped     5656     5585       71 0.012632328
## 7     KY   hosp_ped    21854    21680      174 0.007993752
## 8     MS   hosp_ped    12564    12470       94 0.007509787
## 9     NJ   hosp_ped    20426    20300      126 0.006187693
## 10    MA   hosp_ped    13350    13270       80 0.006010518
## 11    MD   hosp_ped    18441    18334      107 0.005819171
## 12    UT   hosp_ped    10918    10980       62 0.005662618
## 13    VA   hosp_ped    19166    19260       94 0.004892521
## 14    NM   hosp_ped     8390     8351       39 0.004659220
## 15    ID   hosp_ped     4294     4275       19 0.004434590
## 16    CO   hosp_ped    22949    23039       90 0.003914065
## 17    KS   hosp_ped     5109     5128       19 0.003712025
## 18    NV   hosp_ped     5680     5661       19 0.003350675
## 19    AR   hosp_ped    13462    13506       44 0.003263127
## 20    TN   hosp_ped    23438    23376       62 0.002648780
## 21    PA   hosp_ped    57278    57151      127 0.002219717
## 22    RI   hosp_ped     3719     3727        8 0.002148805
## 23    MT   hosp_ped     3511     3518        7 0.001991748
## 24    IL   hosp_ped    46107    46022       85 0.001845239
## 25    PR   hosp_ped    24430    24470       40 0.001635992
## 26    MO   hosp_ped    41624    41556       68 0.001635008
## 27    SD   hosp_ped     4436     4443        7 0.001576754
## 28    NC   hosp_ped    31811    31761       50 0.001573020
## 29    AL   hosp_ped    21961    21929       32 0.001458191
## 30    GA   hosp_ped    55159    55238       79 0.001431198
## 31    AK   hosp_ped     2922     2926        4 0.001367989
## 32    KY hosp_adult   743511   741160     2351 0.003167032
## 33    NC hosp_adult  1376148  1374437     1711 0.001244099
## 34    VT hosp_adult    25440    25466       26 0.001021491
## 
## 
## 
## Raw file for cdcHosp:
## Rows: 50,717
## Columns: 135
## $ state                                                                        <chr> …
## $ date                                                                         <date> …
## $ critical_staffing_shortage_today_yes                                         <dbl> …
## $ critical_staffing_shortage_today_no                                          <dbl> …
## $ critical_staffing_shortage_today_not_reported                                <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_yes                       <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_no                        <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_not_reported              <dbl> …
## $ hospital_onset_covid                                                         <dbl> …
## $ hospital_onset_covid_coverage                                                <dbl> …
## $ inpatient_beds                                                               <dbl> …
## $ inpatient_beds_coverage                                                      <dbl> …
## $ inpatient_beds_used                                                          <dbl> …
## $ inpatient_beds_used_coverage                                                 <dbl> …
## $ inp                                                                          <dbl> …
## $ inpatient_beds_used_covid_coverage                                           <dbl> …
## $ previous_day_admission_adult_covid_confirmed                                 <dbl> …
## $ previous_day_admission_adult_covid_confirmed_coverage                        <dbl> …
## $ previous_day_admission_adult_covid_suspected                                 <dbl> …
## $ previous_day_admission_adult_covid_suspected_coverage                        <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed                             <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_coverage                    <dbl> …
## $ previous_day_admission_pediatric_covid_suspected                             <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_coverage                    <dbl> …
## $ staffed_adult_icu_bed_occupancy                                              <dbl> …
## $ staffed_adult_icu_bed_occupancy_coverage                                     <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid                     <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage            <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid                                   <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_coverage                          <dbl> …
## $ hosp_adult                                                                   <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage     <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid                            <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_coverage                   <dbl> …
## $ hosp_ped                                                                     <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid                        <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage               <dbl> …
## $ total_staffed_adult_icu_beds                                                 <dbl> …
## $ total_staffed_adult_icu_beds_coverage                                        <dbl> …
## $ inpatient_beds_utilization                                                   <dbl> …
## $ inpatient_beds_utilization_coverage                                          <dbl> …
## $ inpatient_beds_utilization_numerator                                         <dbl> …
## $ inpatient_beds_utilization_denominator                                       <dbl> …
## $ percent_of_inpatients_with_covid                                             <dbl> …
## $ percent_of_inpatients_with_covid_coverage                                    <dbl> …
## $ percent_of_inpatients_with_covid_numerator                                   <dbl> …
## $ percent_of_inpatients_with_covid_denominator                                 <dbl> …
## $ inpatient_bed_covid_utilization                                              <dbl> …
## $ inpatient_bed_covid_utilization_coverage                                     <dbl> …
## $ inpatient_bed_covid_utilization_numerator                                    <dbl> …
## $ inpatient_bed_covid_utilization_denominator                                  <dbl> …
## $ adult_icu_bed_covid_utilization                                              <dbl> …
## $ adult_icu_bed_covid_utilization_coverage                                     <dbl> …
## $ adult_icu_bed_covid_utilization_numerator                                    <dbl> …
## $ adult_icu_bed_covid_utilization_denominator                                  <dbl> …
## $ adult_icu_bed_utilization                                                    <dbl> …
## $ adult_icu_bed_utilization_coverage                                           <dbl> …
## $ adult_icu_bed_utilization_numerator                                          <dbl> …
## $ adult_icu_bed_utilization_denominator                                        <dbl> …
## $ geocoded_state                                                               <lgl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+`                           <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage`                  <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown                         <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+`                           <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_coverage`                  <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown                         <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_coverage                <dbl> …
## $ deaths_covid                                                                 <dbl> …
## $ deaths_covid_coverage                                                        <dbl> …
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses                   <dbl> …
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses                            <dbl> …
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses                 <dbl> …
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used               <dbl> …
## $ previous_week_therapeutic_b_bamlanivimab_courses_used                        <dbl> …
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used             <dbl> …
## $ icu_patients_confirmed_influenza                                             <dbl> …
## $ icu_patients_confirmed_influenza_coverage                                    <dbl> …
## $ previous_day_admission_influenza_confirmed                                   <dbl> …
## $ previous_day_admission_influenza_confirmed_coverage                          <dbl> …
## $ previous_day_deaths_covid_and_influenza                                      <dbl> …
## $ previous_day_deaths_covid_and_influenza_coverage                             <dbl> …
## $ previous_day_deaths_influenza                                                <dbl> …
## $ previous_day_deaths_influenza_coverage                                       <dbl> …
## $ total_patients_hospitalized_confirmed_influenza                              <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid                    <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage           <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_coverage                     <dbl> …
## $ all_pediatric_inpatient_bed_occupied                                         <dbl> …
## $ all_pediatric_inpatient_bed_occupied_coverage                                <dbl> …
## $ all_pediatric_inpatient_beds                                                 <dbl> …
## $ all_pediatric_inpatient_beds_coverage                                        <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4                         <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_coverage                <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17                       <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_coverage              <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11                        <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_coverage               <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown                     <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_coverage            <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid                               <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_coverage                      <dbl> …
## $ staffed_pediatric_icu_bed_occupancy                                          <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_coverage                                 <dbl> …
## $ total_staffed_pediatric_icu_beds                                             <dbl> …
## $ total_staffed_pediatric_icu_beds_coverage                                    <dbl> …
## Warning: One or more parsing issues, see `problems()` for details
## Rows: 36440 Columns: 101
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (2): Date, Location
## dbl (93): MMWR_week, Distributed, Distributed_Janssen, Distributed_Moderna, ...
## lgl  (6): Second_Booster, Administered_Bivalent, Admin_Bivalent_PFR, Admin_B...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: Administered_Bivalent Admin_Bivalent_PFR Admin_Bivalent_MOD Dist_Bivalent_PFR Dist_Bivalent_MOD
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 4
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 1 and at least 1%
## 
## [1] date     name     newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
## [1] state    name     newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
## 
## 
## 
## Raw file for vax:
## Rows: 36,440
## Columns: 101
## $ date                                   <date> 2022-09-28, 2022-09-28, 2022-0…
## $ MMWR_week                              <dbl> 39, 39, 39, 39, 39, 39, 39, 39,…
## $ state                                  <chr> "VA2", "NE", "DE", "WV", "IA", …
## $ Distributed                            <dbl> 8845320, 4747440, 2836355, 4821…
## $ Distributed_Janssen                    <dbl> 626900, 151900, 101200, 170200,…
## $ Distributed_Moderna                    <dbl> 4313180, 1627380, 1078600, 1924…
## $ Distributed_Pfizer                     <dbl> 3898140, 2963760, 1653155, 2719…
## $ Distributed_Novavax                    <dbl> 7100, 4400, 3400, 7000, 12500, …
## $ Distributed_Unk_Manuf                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Dist_Per_100K                          <dbl> 0, 245421, 291277, 269043, 2530…
## $ Distributed_Per_100k_5Plus             <dbl> 0, 263231, 308620, 283773, 2698…
## $ Distributed_Per_100k_12Plus            <dbl> 0, 293521, 337739, 309509, 2983…
## $ Distributed_Per_100k_18Plus            <dbl> 0, 325539, 368266, 336571, 3288…
## $ Distributed_Per_100k_65Plus            <dbl> 0, 1519390, 1501460, 1313760, 1…
## $ vxa                                    <dbl> 7846098, 3451226, 1970701, 2901…
## $ Administered_5Plus                     <dbl> 7845919, 3437511, 1963888, 2896…
## $ Administered_12Plus                    <dbl> 7845897, 3306108, 1902046, 2841…
## $ Administered_18Plus                    <dbl> 7842493, 3086344, 1785352, 2709…
## $ Administered_65Plus                    <dbl> 4223864, 970237, 620955, 983314…
## $ Administered_Janssen                   <dbl> 256285, 95847, 62987, 68294, 18…
## $ Administered_Moderna                   <dbl> 4052239, 1227878, 756774, 12486…
## $ Administered_Pfizer                    <dbl> 3537422, 2118721, 1148333, 1582…
## $ Administered_Novavax                   <dbl> 111, 194, 88, 95, 271, 193, 206…
## $ Administered_Unk_Manuf                 <dbl> 41, 8586, 2519, 2161, 1283, 462…
## $ Admin_Per_100k                         <dbl> 0, 178413, 202380, 161918, 1748…
## $ Admin_Per_100k_5Plus                   <dbl> 0, 190599, 213688, 170493, 1856…
## $ Admin_Per_100k_12Plus                  <dbl> 0, 204408, 226486, 182420, 1987…
## $ Admin_Per_100k_18Plus                  <dbl> 0, 211635, 231806, 189157, 2064…
## $ Admin_Per_100k_65Plus                  <dbl> 0, 310518, 328711, 267925, 3140…
## $ Recip_Administered                     <dbl> 7846098, 3471776, 1946433, 2908…
## $ Administered_Dose1_Recip               <dbl> 3545905, 1393418, 835939, 11911…
## $ Administered_Dose1_Pop_Pct             <dbl> 0.0, 72.0, 85.8, 66.5, 69.5, 62…
## $ Administered_Dose1_Recip_5Plus         <dbl> 3545797, 1385254, 832301, 11881…
## $ Administered_Dose1_Recip_5PlusPop_Pct  <dbl> 0.0, 76.8, 90.6, 69.9, 73.6, 66…
## $ Administered_Dose1_Recip_12Plus        <dbl> 3545781, 1321056, 800850, 11585…
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 0.0, 81.7, 95.0, 74.4, 78.2, 72…
## $ Administered_Dose1_Recip_18Plus        <dbl> 3543854, 1221971, 749018, 10956…
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 0.0, 83.8, 95.0, 76.5, 80.5, 75…
## $ Administered_Dose1_Recip_65Plus        <dbl> 1729338, 313542, 218259, 347461…
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 0.0, 95.0, 95.0, 94.7, 95.0, 95…
## $ vxc                                    <dbl> 2988861, 1257663, 694914, 10549…
## $ vxcpoppct                              <dbl> 0.0, 65.0, 71.4, 58.9, 63.2, 55…
## $ Series_Complete_5Plus                  <dbl> 2988790, 1254345, 693868, 10537…
## $ Series_Complete_5PlusPop_Pct           <dbl> 0.0, 69.5, 75.5, 62.0, 67.3, 59…
## $ Series_Complete_12Plus                 <dbl> 2988784, 1198619, 668526, 10302…
## $ Series_Complete_12PlusPop_Pct          <dbl> 0.0, 74.1, 79.6, 66.1, 71.6, 63…
## $ vxcgte18                               <dbl> 2987373, 1108987, 624194, 97492…
## $ vxcgte18pct                            <dbl> 0.0, 76.0, 81.0, 68.1, 73.7, 66…
## $ vxcgte65                               <dbl> 1528522, 292897, 187725, 316675…
## $ vxcgte65pct                            <dbl> 0.0, 93.7, 95.0, 86.3, 94.7, 88…
## $ Series_Complete_Janssen                <dbl> 233980, 89549, 57869, 61920, 16…
## $ Series_Complete_Moderna                <dbl> 1467192, 424332, 242886, 432285…
## $ Series_Complete_Pfizer                 <dbl> 1287661, 741342, 392832, 559945…
## $ Series_Complete_Novavax                <dbl> 24, 59, 29, 30, 50, 91, 651, 28…
## $ Series_Complete_Unk_Manuf              <dbl> 3, 2100, 861, 589, 643, 1010, 4…
## $ Series_Complete_Janssen_5Plus          <dbl> 233974, 89532, 57865, 61903, 16…
## $ Series_Complete_Moderna_5Plus          <dbl> 1467169, 421449, 242494, 431420…
## $ Series_Complete_Pfizer_5Plus           <dbl> 1287620, 741211, 392619, 559781…
## $ Series_Complete_Unk_Manuf_5Plus        <dbl> 3, 2094, 861, 587, 642, 1010, 4…
## $ Series_Complete_Janssen_12Plus         <dbl> 233973, 89519, 57858, 61901, 16…
## $ Series_Complete_Moderna_12Plus         <dbl> 1467168, 421331, 242451, 431343…
## $ Series_Complete_Pfizer_12Plus          <dbl> 1287616, 685669, 367340, 536395…
## $ Series_Complete_Unk_Manuf_12Plus       <dbl> 3, 2041, 848, 572, 606, 1004, 4…
## $ Series_Complete_Janssen_18Plus         <dbl> 233938, 89441, 57808, 61838, 16…
## $ Series_Complete_Moderna_18Plus         <dbl> 1467129, 421072, 242277, 430941…
## $ Series_Complete_Pfizer_18Plus          <dbl> 1286279, 596543, 323272, 481581…
## $ Series_Complete_Unk_Manuf_18Plus       <dbl> 3, 1877, 808, 533, 554, 996, 43…
## $ Series_Complete_Janssen_65Plus         <dbl> 76712, 7058, 10082, 9689, 14679…
## $ Series_Complete_Moderna_65Plus         <dbl> 835134, 141612, 77853, 162729, …
## $ Series_Complete_Pfizer_65Plus          <dbl> 616669, 143226, 99394, 144029, …
## $ Series_Complete_Unk_Manuf_65Plus       <dbl> 2, 993, 393, 223, 163, 373, 227…
## $ Additional_Doses                       <dbl> 1221492, 673360, 339672, 506667…
## $ Additional_Doses_Vax_Pct               <dbl> 40.9, 53.5, 48.9, 48.0, 55.3, 4…
## $ Additional_Doses_5Plus                 <dbl> 1221487, 673298, 339670, 506630…
## $ Additional_Doses_5Plus_Vax_Pct         <dbl> 40.9, 53.7, 49.0, 48.1, 55.4, 4…
## $ Additional_Doses_12Plus                <dbl> 1221486, 662455, 335972, 504088…
## $ Additional_Doses_12Plus_Vax_Pct        <dbl> 40.9, 55.3, 50.3, 48.9, 56.9, 4…
## $ Additional_Doses_18Plus                <dbl> 1221397, 631848, 322036, 491562…
## $ Additional_Doses_18Plus_Vax_Pct        <dbl> 40.9, 57.0, 51.6, 50.4, 58.8, 4…
## $ Additional_Doses_50Plus                <dbl> 1106281, 400915, 227926, 357348…
## $ Additional_Doses_50Plus_Vax_Pct        <dbl> 46.9, 70.2, 63.2, 61.0, 71.9, 6…
## $ Additional_Doses_65Plus                <dbl> 795759, 230950, 135191, 218704,…
## $ Additional_Doses_65Plus_Vax_Pct        <dbl> 52.1, 78.9, 72.0, 69.1, 80.6, 7…
## $ Additional_Doses_Moderna               <dbl> 646719, 255267, 142652, 234519,…
## $ Additional_Doses_Pfizer                <dbl> 553511, 410083, 191595, 266805,…
## $ Additional_Doses_Janssen               <dbl> 21262, 7100, 5321, 5212, 14337,…
## $ Additional_Doses_Unk_Manuf             <dbl> 0, 887, 103, 129, 181, 180, 106…
## $ Second_Booster                         <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Second_Booster_50Plus                  <dbl> 289139, 153105, 88562, 115683, …
## $ Second_Booster_50Plus_Vax_Pct          <dbl> 26.1, 38.2, 38.9, 32.4, 40.6, 3…
## $ Second_Booster_65Plus                  <dbl> 226537, 105198, 63069, 82695, 2…
## $ Second_Booster_65Plus_Vax_Pct          <dbl> 28.5, 45.6, 46.7, 37.8, 49.3, 4…
## $ Second_Booster_Janssen                 <dbl> 58, 114, 88, 72, 100, 94, 2151,…
## $ Second_Booster_Moderna                 <dbl> 152100, 62272, 43047, 57960, 14…
## $ Second_Booster_Pfizer                  <dbl> 141364, 105351, 52049, 67207, 1…
## $ Second_Booster_Unk_Manuf               <dbl> 1, 342, 27, 35, 139, 45, 4088, …
## $ Administered_Bivalent                  <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Admin_Bivalent_PFR                     <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Admin_Bivalent_MOD                     <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Dist_Bivalent_PFR                      <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Dist_Bivalent_MOD                      <lgl> NA, NA, NA, NA, NA, NA, NA, NA,…
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 6
##   isType tot_cases tot_deaths     new_cases    new_deaths         n
##   <chr>      <dbl>      <dbl>         <dbl>         <dbl>     <dbl>
## 1 before  3.77e+10    5.43e+8 95527960      1036655       57997    
## 2 after   3.75e+10    5.40e+8 94432864      1030862       50133    
## 3 pctchg  7.08e- 3    4.61e-3        0.0115       0.00559     0.136
## 
## 
## Processed for cdcDaily:
## Rows: 50,133
## Columns: 6
## $ date       <date> 2021-03-11, 2021-12-01, 2020-04-08, 2020-02-04, 2021-09-01…
## $ state      <chr> "KS", "ND", "AR", "AR", "ND", "IN", "AR", "AL", "NM", "UT",…
## $ tot_cases  <dbl> 297229, 163565, 1071, 0, 118491, 668765, 56199, 547966, 602…
## $ tot_deaths <dbl> 4851, 1907, 18, 0, 1562, 12710, 674, 11530, 8318, 3787, 107…
## $ new_cases  <dbl> 0, 589, 78, 0, 536, 487, 547, 154, 1509, 0, 2135, 8, 451, 0…
## $ new_deaths <dbl> 0, 9, 0, 0, 1, 7, 11, 5, 6, 0, 100, 0, 5, 0, 0, 7, 8, 39, 1…
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 5
##   isType     inp hosp_adult     hosp_ped          n
##   <chr>    <dbl>      <dbl>        <dbl>      <dbl>
## 1 before 5.19e+7    4.52e+7 1269286      50717     
## 2 after  5.16e+7    4.50e+7 1244077      48456     
## 3 pctchg 5.41e-3    5.18e-3       0.0199     0.0446
## 
## 
## Processed for cdcHosp:
## Rows: 48,456
## Columns: 5
## $ date       <date> 2021-01-13, 2021-01-13, 2021-01-13, 2021-01-10, 2021-01-09…
## $ state      <chr> "DC", "NH", "NV", "RI", "MA", "SD", "RI", "MN", "RI", "SD",…
## $ inp        <dbl> 371, 294, 1817, 449, 2075, 247, 483, 1040, 471, 291, 669, 5…
## $ hosp_adult <dbl> 343, 291, 1810, 446, 2051, 244, 479, 1024, 469, 287, 665, 5…
## $ hosp_ped   <dbl> 28, 3, 7, 3, 24, 3, 4, 16, 2, 4, 4, 108, 2, 1, 0, 7, 0, 1, …
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 9
##   isType      vxa      vxc   vxcpoppct vxcgte65 vxcgt…¹ vxcgte18 vxcgt…²       n
##   <chr>     <dbl>    <dbl>       <dbl>    <dbl>   <dbl>    <dbl>   <dbl>   <dbl>
## 1 before 4.19e+11 1.72e+11 1527989.    4.35e+10 2.26e+6 1.59e+11 1.80e+6 3.64e+4
## 2 after  2.02e+11 8.31e+10 1278920.    2.11e+10 2.00e+6 7.69e+10 1.52e+6 2.88e+4
## 3 pctchg 5.18e- 1 5.16e- 1       0.163 5.16e- 1 1.14e-1 5.16e- 1 1.54e-1 2.09e-1
## # … with abbreviated variable names ¹​vxcgte65pct, ²​vxcgte18pct
## 
## 
## Processed for vax:
## Rows: 28,815
## Columns: 9
## $ date        <date> 2022-09-28, 2022-09-28, 2022-09-28, 2022-09-28, 2022-09-2…
## $ state       <chr> "NE", "DE", "WV", "IA", "ID", "FL", "ND", "AR", "KY", "MI"…
## $ vxa         <dbl> 3451226, 1970701, 2901813, 5515503, 2630561, 39803100, 117…
## $ vxc         <dbl> 1257663, 694914, 1054914, 1994024, 989510, 14697269, 43395…
## $ vxcpoppct   <dbl> 65.0, 71.4, 58.9, 63.2, 55.4, 68.4, 56.9, 55.8, 58.6, 61.4…
## $ vxcgte65    <dbl> 292897, 187725, 316675, 523635, 257641, 4173533, 105759, 4…
## $ vxcgte65pct <dbl> 93.7, 95.0, 86.3, 94.7, 88.6, 92.8, 88.2, 82.3, 87.9, 89.9…
## $ vxcgte18    <dbl> 1108987, 624194, 974923, 1790031, 891754, 13500109, 393211…
## $ vxcgte18pct <dbl> 76.0, 81.0, 68.1, 73.7, 66.6, 78.3, 67.6, 65.5, 68.7, 70.5…
## 
## Integrated per capita data file:
## Rows: 50,346
## Columns: 34
## $ date        <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0…
## $ state       <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"…
## $ tot_cases   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tot_deaths  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_cases   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_deaths  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ inp         <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, …
## $ hosp_adult  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hosp_ped    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxa         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxc         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpoppct   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm         <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA…
## $ ahpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ ahpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm7      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm7      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in Proj4 definition

saveToRDS(cdc_daily_221002, ovrWriteError=FALSE)

The function is run to download and process the latest hospitalization data:

# Run for latest data, save as RDS
indivHosp_20221003 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20221003.csv")
## 
## File ./RInputFiles/Coronavirus/HHS_Hospital_20221003.csv already exists
## File will not be downloaded since ovrWrite is not TRUE
## Rows: 260543 Columns: 128
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr   (11): hospital_pk, state, ccn, hospital_name, address, city, zip, hosp...
## dbl  (114): total_beds_7_day_avg, all_adult_hospital_beds_7_day_avg, all_adu...
## lgl    (2): is_metro_micro, is_corrected
## date   (1): collection_week
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 260,543
## Columns: 128
## $ hospital_pk                                                                        <chr> …
## $ collection_week                                                                    <date> …
## $ state                                                                              <chr> …
## $ ccn                                                                                <chr> …
## $ hospital_name                                                                      <chr> …
## $ address                                                                            <chr> …
## $ city                                                                               <chr> …
## $ zip                                                                                <chr> …
## $ hospital_subtype                                                                   <chr> …
## $ fips_code                                                                          <chr> …
## $ is_metro_micro                                                                     <lgl> …
## $ total_beds_7_day_avg                                                               <dbl> …
## $ all_adult_hospital_beds_7_day_avg                                                  <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_avg                                        <dbl> …
## $ inpatient_beds_used_7_day_avg                                                      <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg                                <dbl> …
## $ inpatient_beds_used_covid_7_day_avg                                                <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg          <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg                        <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg      <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg                    <dbl> …
## $ inpatient_beds_7_day_avg                                                           <dbl> …
## $ total_icu_beds_7_day_avg                                                           <dbl> …
## $ total_staffed_adult_icu_beds_7_day_avg                                             <dbl> …
## $ icu_beds_used_7_day_avg                                                            <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_avg                                          <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg                 <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg                               <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg                          <dbl> …
## $ icu_patients_confirmed_influenza_7_day_avg                                         <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg                <dbl> …
## $ total_beds_7_day_sum                                                               <dbl> …
## $ all_adult_hospital_beds_7_day_sum                                                  <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_sum                                        <dbl> …
## $ inpatient_beds_used_7_day_sum                                                      <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum                                <dbl> …
## $ inpatient_beds_used_covid_7_day_sum                                                <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum          <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum                        <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum      <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum                    <dbl> …
## $ inpatient_beds_7_day_sum                                                           <dbl> …
## $ total_icu_beds_7_day_sum                                                           <dbl> …
## $ total_staffed_adult_icu_beds_7_day_sum                                             <dbl> …
## $ icu_beds_used_7_day_sum                                                            <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_sum                                          <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum                 <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum                               <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum                          <dbl> …
## $ icu_patients_confirmed_influenza_7_day_sum                                         <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum                <dbl> …
## $ total_beds_7_day_coverage                                                          <dbl> …
## $ all_adult_hospital_beds_7_day_coverage                                             <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_coverage                                   <dbl> …
## $ inpatient_beds_used_7_day_coverage                                                 <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage                           <dbl> …
## $ inpatient_beds_used_covid_7_day_coverage                                           <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage     <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage                   <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage               <dbl> …
## $ inpatient_beds_7_day_coverage                                                      <dbl> …
## $ total_icu_beds_7_day_coverage                                                      <dbl> …
## $ total_staffed_adult_icu_beds_7_day_coverage                                        <dbl> …
## $ icu_beds_used_7_day_coverage                                                       <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_coverage                                     <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage            <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage                          <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage                     <dbl> …
## $ icu_patients_confirmed_influenza_7_day_coverage                                    <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage           <dbl> …
## $ previous_day_admission_adult_covid_confirmed_7_day_sum                             <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum`                       <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum                     <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum                         <dbl> …
## $ previous_day_covid_ED_visits_7_day_sum                                             <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_sum                             <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum`                       <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum                     <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum                         <dbl> …
## $ previous_day_total_ED_visits_7_day_sum                                             <dbl> …
## $ previous_day_admission_influenza_confirmed_7_day_sum                               <dbl> …
## $ geocoded_hospital_address                                                          <chr> …
## $ hhs_ids                                                                            <chr> …
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage                        <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage                    <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_coverage                        <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage                    <dbl> …
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day                  <dbl> …
## $ total_personnel_covid_vaccinated_doses_none_7_day                                  <dbl> …
## $ total_personnel_covid_vaccinated_doses_one_7_day                                   <dbl> …
## $ total_personnel_covid_vaccinated_doses_all_7_day                                   <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_one_7_day                            <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_all_7_day                            <dbl> …
## $ is_corrected                                                                       <lgl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_avg                                     <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_coverage                                <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_sum                                     <dbl> …
## $ all_pediatric_inpatient_beds_7_day_avg                                             <dbl> …
## $ all_pediatric_inpatient_beds_7_day_coverage                                        <dbl> …
## $ all_pediatric_inpatient_beds_7_day_sum                                             <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum                     <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum                   <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum                    <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum                 <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_avg                           <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage                      <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_sum                           <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_avg                                      <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_coverage                                 <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_sum                                      <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_avg                                         <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_coverage                                    <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_sum                                         <dbl> …
## 
## Hospital Subtype Counts:
## # A tibble: 4 × 2
##   hospital_subtype               n
##   <chr>                      <int>
## 1 Childrens Hospitals         4909
## 2 Critical Access Hospitals  69937
## 3 Long Term                  17869
## 4 Short Term                167828
## 
## Records other than 50 states and DC
## # A tibble: 5 × 2
##   state     n
##   <chr> <int>
## 1 AS       52
## 2 GU      102
## 3 MP       40
## 4 PR     2766
## 5 VI      104
## 
## Record types for key metrics
## # A tibble: 10 × 5
##    name                                              `NA` Posit…¹ Value…²  Total
##    <chr>                                            <int>   <int>   <int>  <int>
##  1 all_adult_hospital_beds_7_day_avg                71194  188847     502 260543
##  2 all_adult_hospital_inpatient_bed_occupied_7_day…   131  238657   21755 260543
##  3 icu_beds_used_7_day_avg                             56  228646   31841 260543
##  4 inpatient_beds_7_day_avg                            46  259479    1018 260543
##  5 inpatient_beds_used_7_day_avg                       35  239484   21024 260543
##  6 inpatient_beds_used_covid_7_day_avg                 26  173367   87150 260543
##  7 staffed_icu_adult_patients_confirmed_and_suspec…   169  174858   85516 260543
##  8 total_adult_patients_hospitalized_confirmed_and…   127  173350   87066 260543
##  9 total_beds_7_day_avg                             69419  190826     298 260543
## 10 total_icu_beds_7_day_avg                            58  246878   13607 260543
## # … with abbreviated variable names ¹​Positive, ²​`Value -999999`
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

saveToRDS(indivHosp_20221003, ovrWriteError=FALSE)

Post-processing is run, including hospital summaries:

# Create pivoted burden data
burdenPivotList_221002 <- postProcessCDCDaily(cdc_daily_221002, 
                                              dataThruLabel="Sep 2022", 
                                              keyDatesBurden=c("2022-09-30", "2022-03-31", 
                                                               "2021-09-30", "2021-03-31"
                                                               ),
                                              keyDatesVaccine=c("2022-09-28", "2022-03-31", 
                                                                "2021-09-30", "2021-03-31"
                                                                ), 
                                              returnData=TRUE
                                              )
## Joining, by = "state"
## 
## *** File has been checked for uniqueness by: state date name
## Warning: Removed 24 row(s) containing missing values (geom_path).

## Warning: Removed 24 rows containing missing values (position_stack).

## Warning: Removed 24 rows containing missing values (position_stack).

## Warning: Removed 9 row(s) containing missing values (geom_path).

# Create hospitalized per capita data
hospPerCap_221002 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"), 
                                      lst=burdenPivotList_221002, 
                                      popVar="pop2019", 
                                      excludeState=c(), 
                                      cumStartDate="2020-07-15"
                                      )
## Warning: Removed 18 row(s) containing missing values (geom_path).

burdenPivotList_221002$hospAge %>%
    group_by(adultPed, confSusp, age, name) %>%
    summarize(value=sum(value, na.rm=TRUE), n=n(), .groups="drop")
## # A tibble: 18 × 6
##    adultPed confSusp  age   name                                     value     n
##    <chr>    <chr>     <chr> <chr>                                    <dbl> <int>
##  1 adult    confirmed 0-19  previous_day_admission_adult_covid_con… 4.77e4 50717
##  2 adult    confirmed 20-29 previous_day_admission_adult_covid_con… 2.90e5 50717
##  3 adult    confirmed 30-39 previous_day_admission_adult_covid_con… 4.19e5 50717
##  4 adult    confirmed 40-49 previous_day_admission_adult_covid_con… 5.02e5 50717
##  5 adult    confirmed 50-59 previous_day_admission_adult_covid_con… 8.01e5 50717
##  6 adult    confirmed 60-69 previous_day_admission_adult_covid_con… 1.05e6 50717
##  7 adult    confirmed 70-79 previous_day_admission_adult_covid_con… 1.06e6 50717
##  8 adult    confirmed 80+   previous_day_admission_adult_covid_con… 9.58e5 50717
##  9 adult    suspected 0-19  previous_day_admission_adult_covid_sus… 3.91e4 50717
## 10 adult    suspected 20-29 previous_day_admission_adult_covid_sus… 2.61e5 50717
## 11 adult    suspected 30-39 previous_day_admission_adult_covid_sus… 3.43e5 50717
## 12 adult    suspected 40-49 previous_day_admission_adult_covid_sus… 3.47e5 50717
## 13 adult    suspected 50-59 previous_day_admission_adult_covid_sus… 5.48e5 50717
## 14 adult    suspected 60-69 previous_day_admission_adult_covid_sus… 7.55e5 50717
## 15 adult    suspected 70-79 previous_day_admission_adult_covid_sus… 7.36e5 50717
## 16 adult    suspected 80+   previous_day_admission_adult_covid_sus… 6.70e5 50717
## 17 ped      confirmed 0-19  previous_day_admission_pediatric_covid… 1.73e5 50717
## 18 ped      suspected 0-19  previous_day_admission_pediatric_covid… 3.88e5 50717
saveToRDS(burdenPivotList_221002, ovrWriteError=FALSE)
saveToRDS(hospPerCap_221002, ovrWriteError=FALSE)

Peaks and valleys of key metrics are also updated:

peakValleyCDCDaily(cdc_daily_221002)
## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 20 row(s) containing missing values (geom_path).

## Warning: Removed 20 row(s) containing missing values (geom_path).

## # A tibble: 8,040 × 8
##    date       state   vxa   vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
##    <date>     <chr> <dbl> <dbl> <lgl>      <lgl>      <lgl>        <lgl>       
##  1 2020-12-01 CA       NA    NA FALSE      FALSE      FALSE        FALSE       
##  2 2020-12-01 FL       NA    NA FALSE      FALSE      FALSE        FALSE       
##  3 2020-12-01 GA       NA    NA FALSE      FALSE      FALSE        FALSE       
##  4 2020-12-01 IL       NA    NA FALSE      FALSE      FALSE        FALSE       
##  5 2020-12-01 MI       NA    NA FALSE      FALSE      FALSE        FALSE       
##  6 2020-12-01 NC       NA    NA FALSE      FALSE      FALSE        FALSE       
##  7 2020-12-01 NJ       NA    NA FALSE      FALSE      FALSE        FALSE       
##  8 2020-12-01 NY       NA    NA FALSE      FALSE      FALSE        FALSE       
##  9 2020-12-01 OH       NA    NA FALSE      FALSE      FALSE        FALSE       
## 10 2020-12-01 PA       NA    NA FALSE      FALSE      FALSE        FALSE       
## # … with 8,030 more rows
## # ℹ Use `print(n = ...)` to see more rows